18491317. ROOT CAUSE ANALYSIS USING GRANGER CAUSALITY simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
Contents
- 1 ROOT CAUSE ANALYSIS USING GRANGER CAUSALITY
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ROOT CAUSE ANALYSIS USING GRANGER CAUSALITY - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
ROOT CAUSE ANALYSIS USING GRANGER CAUSALITY
Organization Name
INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor(s)
Karthikeyan Shanmugam of Elmsford NY (US)
Bhanukiran Vinzamuri of Elmsford NY (US)
ROOT CAUSE ANALYSIS USING GRANGER CAUSALITY - A simplified explanation of the abstract
This abstract first appeared for US patent application 18491317 titled 'ROOT CAUSE ANALYSIS USING GRANGER CAUSALITY
Simplified Explanation
The abstract describes a system that uses time series data to detect the root cause of failure in a mechanical system by employing a greedy hill climbing process to perform conditional independence tests.
- The system comprises a memory to store computer executable components and a processor to execute these components.
- The maintenance component in the computer executable components detects the cause of failure by analyzing Granger causality between variables from time series data of the mechanical system.
Potential Applications
This technology can be applied in various industries such as manufacturing, automotive, aerospace, and healthcare for predictive maintenance and fault detection in mechanical systems.
Problems Solved
1. Efficient root cause analysis of failures in mechanical systems. 2. Early detection of potential issues in equipment to prevent costly downtime.
Benefits
1. Improved reliability and performance of mechanical systems. 2. Cost savings through proactive maintenance. 3. Enhanced safety by identifying potential failures before they occur.
Potential Commercial Applications
Predictive maintenance software for industrial machinery. Fault detection systems for automotive vehicles. Health monitoring solutions for medical equipment.
Possible Prior Art
One possible prior art could be traditional root cause analysis methods that rely on manual inspection and historical data rather than time series analysis for failure detection.
What are the limitations of the proposed system in detecting root causes of failure in mechanical systems?
The abstract does not mention the accuracy or reliability of the system in detecting root causes of failure in complex mechanical systems.
How does the proposed system compare to existing methods of root cause analysis in terms of computational efficiency?
The abstract does not provide information on the computational efficiency of the proposed system compared to traditional root cause analysis methods.
Original Abstract Submitted
Techniques regarding root cause analyses based on time series data are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise maintenance component that can detect a cause of failure for a mechanical system by employing a greedy hill climbing process to perform a polynomial number of conditional independence tests to determine a Granger causality between variables from time series data of the mechanical system given a conditioning set.
